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Best Way to Build a Searchable Document Index?
Blinocac writes "I am organizing the IT documentation for the agency I work for, and we would like to make a searchable document index that would render results based on meta tags placed in the documents, which include everything from Word files, HTML, Excel, Access, and PDF's." What methods or tools have others seen that work? Anything to avoid?
Check out Apache's free Lucene engine, found at lucene.apache.org/. Lucene is a powerful indexing engine that handles all kinds of docs, and you can easily mod it to handle whatever it doesn't. It also allows custom scoring and a very powerful query language.
We have a Google appliance, but you can do it with regular Google, too. Just make sure you disable caching (with headers or by encrypting documents). Then place an IP or password restriction for non-Google crawlers (check IP, not user-agent). People will be able to search with the power of Google, but only people you allow in will be able to get the full documents.
If you value your privacy, invest in a Google mini, though.
http://www.swish-e.org/
it wil cost you some bucks just buy MS sharepoint portal server, and leave the indexing over to sharepoint.
Your not even realy required to use added tags... (as most people will put in poor tags).
But if you like you can add tags even with sharepoint.
I know you're out there. I can feel you now. I know that you're afraid. You're afraid of us. You're afraid of change.
I'd suggest you should consider a full-text search engine. First start here:
http://en.wikipedia.org/wiki/Full_text_search
If you're not afraid to do a little reading and potentially coding a custom front end, you may want to look at two of the big open source engines: Lucene and Xapian.
Lucene is quite popular now, and is an Apache Java project. It's a good choice if you're a Java shop.
Xapian seems to be based on a little more solid and modern information retrieval theory and is incredibly scalable and fast. It's written in C++, with SWIG-based front ends to many languages. It might not have as polished of a front end or as fancy of a website as Lucene, but I believe it's a better choice if you have really really huge data sets or want to venture outside the Java universe.
There are also many other wholely-contained indexers too, mostly which are based on web indexing (they have spiders, query forms, etc.) all bundled together. Like ht://Dig, mnogosearch, and so forth. They are good, especially if you want more of a drop-in solution rather than a raw indexing engine, and if you're indexing web sites (and not complex entities like databases, etc).
Yep, a Google appliance (or equivalent, there are others on the market such as X1) is the way to go.
I set up a Google Mini for indexing an internal wiki, our bug tracking system, and some other systems, and it is very straight-forward.
I know the original question mentioned meta-data, but you have to ask yourself if the meta-data is going to be maintained well enough that the search index will be valid. Going the Google Appliance route is so much simpler. It takes a bit of tweaking to set up the search restrictions, but once up and running, it works flawlessly. Most importantly, it doesn't require everyone to make sure that all their document meta-data is perfect.
Google appliance pricing is really quite cheap when you compare it to the time cost of setting up a meta-data driven system.
Meta-data is one of those things that seems like a really good idea, but like all plans, doesn't tend to survive contact with the enemy, which in this case is the user.
Paul
Paul Leader
If you host all of your documentation on a website, take a look at ht://dig [http://www.htdig.org].
/cgi-bin/search.cgi to your page, and you can auto-magically search your documentation.
I've deployed it across a handful of servers, and it does a good job of crawling, but doesn't do well with javascript. If you have javascript for your web's frontend, you can write a shell script to find . -print, prepend the urls into a file, and point htdig at that file. It will dig into each file it finds, and create a searchable database of everything that it finds.
You add
- Avron
There are 2 problems: getting plain text out of documents, then indexing the plain text
A good tool for getting plain text out of various versions of Word documents is the "antiword" command line utility.
The Apache POI project (Java) can read and write several Microsoft Office formats.
For indexing: I like Lucene (Java), Ferret (Ruby+C), and Montezuma (Common Lisp).
I have mostly been using Ruby the last few years for text processing. Here is a short article I wrote using the Java Lucene library using JRuby:
http://markwatson.com/blog/2007/06/using-lucene-with-jruby.html
Here is another short snippet for reading OpenOffice.org documents in Ruby:
http://markwatson.com/blog/2007/05/why-odf-is-better-than-microsofts.html
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You might just want to use the entire Nutch stack:
http://lucene.apache.org/nutch/
stack that collects documents, spiders the web, has plugins for many document types, etc. Good stuff!
As a Documentum developer, especially in light of the recent 6.0 release, I'd be remiss not to recommend it for such a purpose. It's expensive, rather complex, and requires solid development talent to implement, but is almost infinitely configurable and customizable, and there are separate components (at cost, of course) that can add on all sorts of fun functionality like collaboration, digital asset management, etc. It has the ability to auto-tag documents based on configurable rules using Content Intelligence Services and supports extensible object hierarchies, workflows, lifecycles, taxonomies, web services, you name it. It's probably overkill for the user in question, and it's far from open source (although EMC is doing an admirable job at encouraging code exchange, and the new dev. environment is based on Eclipse), but it's pretty darn slick when you look at the ground it covers, functionally.
If you're looking for an index, a document management system probably makes sense. This one is inexpensive and very good.
Sorry, mangled the URL in the parent: Wumpus-Search.org
The parent here speaks the truth.
:) I'd recommend installing the base Alfresco Community release (no need for Web Content Management, Records Management etc to start with), loading some docs into it via the FTP interface (or upload a zip via the web interface which it will explode out for you) & giving it a test run. I've got people asking me every couple of days when we're rolling it out internally (just got to finish the sharepoint comparison first).
I notice a lot of the comments in the thread are coming from developers or sysadmins who want to solve everything with libraries or command line tools. But it really sounds to me like you need a reasonable document management system (and of course being a slashdot reader you want it for free).
Again, I'm not affiliated with Alfresco, but did quite a bit of research into open source DMS's that would run in a java environment for a couple of recent projects. I found Alfresco to be well architected, easily extendible if I needed it to be and importantly simple to deploy & get running. It will integrate with your LDAP for access and while it's marketed as an Enterprise CMS, is quite capable of doing DMS.
It uses Lucene under the hood, and while it has a web UI, isn't focused on indexing web sites. You can record meta-data against docs, and it's also capable of extracting some metadata from common MS Office formats. I've no doubt this could be extended if there were other doc properties you wanted access to (although I've never tried myself).
Most importantly is that the project & community is quite healthy with very active forums. You can get paid support (the Enterprise License) if you so desire, but I expect you'd probably start with the GPL version just to get yourself up & running.
I wouldn't recommend the SMB interface for the time being as there's currently an outstanding bug with it that causes it to die after a while (the rest of the app continues to run happily), however the FTP interface is great for an initial import of docs. Also take a look at the rules capability for classifying/sorting docs as they're imported.
It does the basics like check-in/check-out & workflow, and can be backed by your DB of choice as it uses Hibernate for ORM. Searching can be done against keywords or meta-data (classifications, dates, authors etc) & in my experience is more powerful/useful than sharepoints keyword based searching. If you're really keen you can use the Java or Web Service API's for integrating into other solutions.
Again, I'm not affiliated, but clearly I'm a fan-boy
Terrier - LINK
Indri/Lemur - LINK / LINK
MG - LINK
Unless you can pin responsibility for a document to a named person, you can't trust anything in the document. Not metadata, not content, not presentation.
The meta tags most of the documents I deal with are inserted by the applications, and only the content is human-drafted. Those meta tags contain information like creation date, mdification date, application name, character encoding, etc. They are generally trustworthy.
I'm also in the process of building a documentation system; it will be a set of documents in various formats, with an HTML interface, TomCat server and Lucene to make it fully searchable.
In a previous job, I did a similar thing with Apache and ht://dig on an old Dell I recycled. Document files could be uploaded by anybody with an FTP account on the server, and index files were automatically regenerated by a CRON task at 04h00 each day.
I could have made a trigger to regenerate the index after each FTP upload session, but using CRON was easier and sufficiently frequent to be useful.
This time around, the whole system of TomCat webserver and Lucene search engine is bundled on a CD-ROM with the docs to run on any of the firm's laptops. Because I control the documents, I can build the index files and burn them to the CD-ROM before distribution.
Beef
As someone who has made a 30-year career out of designing and building document management systems, I would urge you to look first at how you expect your users to find the documents they need. The expected results of a search should guide your choice of indexing methods - and the popular "meta tagging" method isn't always the best. There are shortcomings with all methods.
Full-text indexing allows users to search the entire contents of documents, but the results are imprecise and voluminous and not terribly useful in most cases (think web search engines here). Yes, you can find all documents that contain the word "patent", but you get a lot of old references to patent leather shoes in addition to what you were probably after. So, with full-text search you get it all, but force the user to subsearch for what they really want.
Using meta-tags gives the appearance of pre-classifying documents and having the users do it themselves means you don't have to have a dedicated person to assign the tags. The disadvantage is that everybody makes up their own tags or if you have a standard set, you have to rely on people being diligent about applying them. And tag popularity can easily change over time. For example, if you want to find docs that refer to "removable media", this might have garnered a "floppy" tag 15 years ago and "CD" or "DVD" today. You are therefore almost guaranteed of missing some documents using this method.
Database indexing means that you list all your docs in a database, perhaps by title, author, date, or other fields that your users would find useful for searching. The advantage is that every doucment is indexed the same way, searching is really fast, and the results are usually relevant if your schema is meaningful. The disadvantages are that indexing the docs takes work on input and users need to know how to search to get the best results.
Finally, you could organize the docs by simple name and folder. This works fine for the desktop and users usually can identify the category that points them to the folder they want. The disadvantage is that this only works well for limited document sets. Once you start getting hundreds of categories and thousands and thousands of documents, things become too hard to find.
So - understand your users search requirements and the size of your expected database. Only then can you make an informed decision about how to create and index the repository.